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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Chen, Xianda Yuan, Xinyi Zhu, Meixin Zheng, Xinhu Shen, Shaojie Wang, Xuesong Wang, Yinhai Wang, Fei-Yue Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR China School of Transportation Engineering Tongji University Shanghai China Department of Civil and Environmental Engineering University of Washington Seattle WA USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Car-following is the most common driving scenario where a following vehicle follows a lead vehicle in the same lane. One crucial factor of car-following behavior is driving style which affects speed and gap selection, acceleration pattern, and fuel consumption. However, existing car-following research used limited categories of driving style through pre-defined patterns and failed to encode driving style into data-driven car-following models. To address these limitations, we propose the Aggressiveness Informed Car-Following (AICF) modeling approach, which embeds driving style as a dynamic input feature in data-driven car-following models. In detail, We design driving aggressiveness tokens using four physical quantities (jerk, acceleration, relative speed, and relative spacing) to capture the heterogeneity of driving aggressiveness. These tokens were then embedded into a physics-informed Long Short-Term Memory (LSTM) based car-following model for trajectory prediction. To evaluate the effectiveness of our approach, we conducted extensive experiments based on 12,540 car-following events extracted from the HighD dataset and 24,093 events from the Lyft dataset. Compared to models devoid of considerations for driving aggressiveness levels, AICF exhibits superior efficacy in mitigating the Mean Square Error (MSE) of spacing and collision rate. To the best of our knowledge, this is the first work to directly incorporate real-time driving aggressiveness tokens as input features into data-driven car-following models, enabling a more comprehensive understanding of aggressiveness in car-following behavior. IEEE

关键词： Long short-term memory

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Preferential attachment network model with aging and initial attractiveness

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Communications in Theoretical Physics 2022年第3期74卷 97-103页

作者： Xiao-Long Peng Complex Systems Research Center Shanxi UniversityTaiyuan 030006ShanxiChina Shanxi Center for Applied Mathematics Shanxi UniversityTaiyuan 030006ShanxiChina Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention Shanxi UniversityTaiyuan 030006ShanxiChina

In this paper,we generalize the growing network model with preferential attachment for new links to simultaneously include aging and initial attractiveness of *** network evolves with the addition of a new node per unit time,and each new node has m new links that with probability Π_(i) are connected to nodes i already present in the *** our model,the preferential attachment probability Π_(i) is proportional not only to k_(i)+A,the sum of the old node i's degree ki and its initial attractiveness A,but also to the aging factor τ_(i)^(−α),whereτi is the age of the old node *** is,Π_(i)∝(k_(i)+A)τ_(i)^(−α).Based on the continuum approximation,we present a mean-field analysis that predicts the degree dynamics of the network *** show that depending on the aging parameter α two different network topologies can *** α<1,the network exhibits scaling behavior with a power-law degree distribution P(k)∝k^(−γ) for large k where the scaling exponent γ increases with the aging parameter α and is linearly correlated with the ratio A/***,the average degree k(ti,t)at time t for any node i that is added into the network at time ti scales as k(t_(i),t)∝t_(i)^(−β) where 1/β is a linear function of A/*** α>1,such scaling behavior disappears and the degree distribution is exponential.

关键词： complex network model aging preferential attachment power-law behavior

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Trajectory Planning Towards Water-Air Trans-Media Motion for Robotic Dolphin

Trajectory Planning Towards Water-Air Trans-Media Motion for...

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2024 China Automation Congress, CAC 2024

作者： Xiong, Yan Zhang, Pengfei Chen, Di Yu, Junzhi State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing100871 China Shandong Nuclear Power Company LTD Yantai265116 China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9798350368604

Water-air trans-media navigators have multi-media motion modes, breaking through the limitation that traditional robots can only operate in a single medium. Autonomous navigation is an indispensable component of the process of trans-media motion. Most of the current work has only completed the development and testing of prototypes, and the problem of task-oriented motion planning and control needs to be solved. In this paper, a safe and efficient hierarchical approach is proposed. First, a novel rapidly exploring random tree-based algorithm is proposed to search for a suitable path. Then, by fixing bound waypoints, the time-optimization trajectory is planned. Finally, the trajectory tracker is designed based on nonlinear model predictive control (NMPC). Simulation experiments numerically validate the proposed method, offering some new insights for further trans-media motions. © 2024 IEEE.

关键词： Trajectories

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Load balancing strategy for wireless multi-hotspot networks based on improved NSGA-II algorithm

Load balancing strategy for wireless multi-hotspot networks ...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Yu, Han Zhan, Jie Libin, Jiao Han, Jun-Mei School of Physics and Electronic Science Hunan University of Science and Technology Xiangtan412001 China Science and Technology on Communication Networks Laboratory Shijiazhuang050081 China National Key Laboratory for Complex Systems Simulation Department of Systems General Design Institute of Systems Engineering Beijing100101 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

IEEE 802.11 Wireless Local Area Network (WLAN) is a popular way for mobile devices to access the Internet. Operators and service providers often increase the density of wireless access points in order to provide users with better connections and user experience. As a result, WLAN users find themselves covered by multiple access points. In traditional wireless multi-hotspot networks most user terminals choose the access point with the strongest signal without considering the existing load on that access point, leading to severe congestion and load imbalance problems. This paper proposes an improved NSGA-II-based load balancing strategy for wireless multi-hotspot networks, which determines which hotspot should be associated with each user terminal by taking into account the current access point load, the received signal strength and the throughput size of the user terminal. The simulation results show that compared with the traditional strongest signal First (SSF) strategy, the proposed load balancing strategy can improve the network throughput by more than 30%. © 2022 IEEE.

关键词： IEEE Standards

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Hierarchical Graph Signal Processing for Collaborative Filtering 24

Hierarchical Graph Signal Processing for Collaborative Filte...

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33rd ACM Web Conference, WWW 2024

作者： Xia, Jiafeng Li, Dongsheng Gu, Hansu Lu, Tun Zhang, Peng Shang, Li Gu, Ning Fudan University Shanghai China Microsoft Research Asia Shanghai China Seattle United States School of Computer Science and Shanghai Key Laboratory of Data Science Fudan University China Fudan Institute on Aging MOE Laboratory for National Development and Intelligent Governance Shanghai Institute of Intelligent Electronics and Systems Fudan University China

ISBN: (纸本)9798400701719

Graph Signal Processing (GSP) has proven to be a highly effective and efficient tool for predicting user future interactions in recommender systems. However, current GSP methods recognize user interaction patterns based on the interactions of all users, so that the recognized interaction patterns are not fully user-matched and easily impacted by other users with different interaction behaviors, resulting in sub-optimal recommendation performance. To this end, we propose a hierarchical graph signal processing method (HiGSP) for collaborative filtering, which consists of two key modules: 1) the cluster-wise filter module that recognizes user unique interaction patterns merely from interactions of users with similar preferences, making the recognized patterns able to reflect user preference without being influenced by other users with different interaction behaviors, and 2) the globally-aware filter module that serves as a complementary to the cluster-wise filter module to recognize user general interaction patterns more effectively from all user interactions. By linearly combining these two modules, HiGSP can recognize user-matched interaction patterns, so as to model user preference and predict user future interactions more accurately. Extensive experiments on six real-world datasets demonstrate the superiority of HiGSP compared to other GCN-based and GSP-based recommendation methods in terms of efficacy and efficiency. © 2024 ACM.

关键词： Collaborative filtering

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Scene-adaptive and Region-aware Multi-modal Prompt for Open Vocabulary Object Detection

Scene-adaptive and Region-aware Multi-modal Prompt for Open ...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiaowei Zhao Xianglong Liu Duorui Wang Yajun Gao Zhide Liu State Key Laboratory of Complex & Critical Software Environment Beihang University Zhongguancun Laboratory Institute of data space Heifei Comprehensive National Science Center

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Open Vocabulary Object Detection (OVD) aims to detect objects from novel classes described by text inputs based on the generalization ability of trained classes. Existing methods mainly focus on transferring knowledge from large Vision and Language models (VLM) to detectors through knowledge distillation. However, these approaches show weak ability in adapting to diverse classes and aligning be-tween the image-level pre-training and region-level detection, thereby impeding effective knowledge transfer. Moti-vated by the prompt tuning, we propose scene-adaptive and region-aware multi-modal prompts to address these issues by effectively adapting class-aware knowledge from VLM to the detector at the region level. Specifically, to enhance the adaptability to diverse classes, we design a scene-adaptive prompt generator from a scene perspective to consider both the commonality and diversity of the class distributions, and formulate a novel selection mechanism to facilitate the ac-quisition of common knowledge across all classes and spe-cific insights relevant to each scene. Meanwhile, to bridge the gap between the pre-trained model and the detector, we present a region-aware multi-modal alignment module, which employs the region prompt to incorporate the po-sitional information for feature distillation and integrates textual prompts to align visual and linguistic representations. Extensive experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art models on the OV-COCO and OV-LVIS datasets, sur-passing the current method by 3.0% mAP and 4.6% APr.

关键词： Vocabulary Visualization Adaptation models Semantics Detectors Object detection Linguistics

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Sustainable Mining in the Era of Artificial Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 1-4页

作者： Long Chen Yuting Xie Yutong Wang Shirong Ge Fei-Yue Wang IEEE the State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of SciencesBeijing 100190China the National Laboratory of Pattern Recognition at the Institute of Automation and Waytous Ltd.China the School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275GuangdongChina the School of Mechanical Electronic and Information Engineering China University of Mining and Technology BeijingBejing 100083China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China

The mining sector historically drove the global economy but at the expense of severe environmental and health repercussions,posing sustainability challenges[1]-[3].Recent advancements on artificial intelligence(AI)are revolutionizing mining through robotic and data-driven innovations[4]-[7].While AI offers mining industry advantages,it is crucial to acknowledge the potential risks associated with its widespread ***-reliance on AI may lead to a loss of human control over mining operations in the future,resulting in unpredictable consequences.

关键词： Sustainable mining consequences

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A Deep Reinforcement Learning Approach for Cooperative Target Defense 7th

A Deep Reinforcement Learning Approach for Cooperative Targ...

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7th International Conference on data Mining and Big data, DMBD 2022

作者： Xiong, Yanxue Wang, Zhigang Ke, Liangjun State Key Laboratory for Manufacturing Systems Engineering School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China CETC Key Laboratory of Data Link Technology Xi’an Xi’an China

ISBN: (纸本)9789811992964

This paper considers a new variant of the pursuit-evasion problem, called the cooperative target defense problem with three agents (attacker, targeter, and defender) in a 3D space. The targeter tries to fly as quickly as possible from a starting point to the terminal, while the defender seeks to protect it from the attacker. The problem is difficult to solve under traditional game theory methods, while deep reinforcement learning (DRL) has shown strong adaptability in these complex and higher-dimensional tasks. Inspired by the successful applications of Proximal Policy Optimization (PPO), this paper proposes a PPO-based algorithm for the problem, intending to derive the optimal behavioral policies for both sides. We design the corresponding state space, action space, and rewards of the agents. Three kinds of reward functions are proposed for the attacker and compared by experimental results. Our study provides a good foundation for the cooperative target defense problem. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Pattern Matching of Industrial Alarm Floods Using Word Embedding and Dynamic Time Warping

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1096-1098页

作者： Wenkai Hu Xiangxiang Zhang Jiandong Wang Guang Yang Yuxin Cai School of Automation China University of GeosciencesWuhan 430074Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhan 430074 Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China

Dear Editor,This letter proposes a new pattern matching method based on word embedding and dynamic time warping(DTW)to identify groups of similar alarm ***,alarm messages are transformed into numeric values that represent alarms and also reflect the relationships between alarm ***,similarities between numerically encoded alarm flood sequences are calculated by DTW and groups of similar floods are identified via *** effectiveness of the proposed method is demonstrated by a case study with alarm&event data obtained from a public industrial simulation model.

关键词： Word Alarm DTW

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Particle Swarm Based Reinforcement Learning 7th

Particle Swarm Based Reinforcement Learning

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7th International Conference on data Mining and Big data, DMBD 2022

作者： Duan, Jianyu Guo, Yanxiao Wang, Zhigang Ke, Liangjun State Key Laboratory for Manufacturing Systems Engineering School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China CETC Key Laboratory of Data Link Technology Xi’an Xi’an China

ISBN: (纸本)9789811992964

With the vigorous development of computer-related technology, the "perception + decision" paradigm of the combination of deep learning and reinforcement learning has become a research hotspot. Nowadays, deep reinforcement learning algorithms have been successfully applied to the fields of games, industry and commerce. However, deep reinforcement learning algorithms often fall into the dilemma of "exploration" and "exploitation", and the effect of these algorithms is easily affected by the quality of hyperparameters. In order to make up for the defects mentioned above, this paper introduces the particle swarm based reinforcement learning framework (PRL). Compared with the standard reinforcement learning algorithms, this framework greatly improves the exploration ability and obtains better scores in a series of gym experimental tests. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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